Computer vision is an interdisciplinary field that deals with means of gaining high-level understanding from digital images or videos using computers instead of, and/or complementary to, a human visual system. Computer vision involves acquiring, processing, analyzing, and understanding digital images, as well as extracting high-dimensional data from real-world imagery, to produce numerical or symbolic information. As a scientific discipline, computer vision is concerned with the theory behind artificial systems that extract information from images and includes the fields of image registration, change detection, and pattern recognition in remotely sensed imagery.
Computer vision systems typically employ algorithms (e.g., computer-implemented instructions) that operate on input image data. The input image data may be captured by any number of imaging sensors currently known in the art, each with potentially varying performance capabilities for remote collection of image data. Depending on a particular sensor's design, the configuration of a hosting platform, and the sensing environment, a variety of environmental, sensor, system, and other uncertainties can corrupt the images collected so that post-processing techniques (such as similarity measures for image registration and template matching in pattern recognition) can be affected. A computer vision solution manufactured to operate in a first scenario (e.g., component design, hosting platform, sensing environment), but deployed in a second scenario can lead to algorithm failure, which can be expensive, mission-threatening, or both.
To improve the versatility of a computer vision design, the algorithm employed and the system in which the algorithm operates should implement methods and designs making them more resilient. That is, both should be able to receive data from different sensors, in different environments, on different platforms, and still perform effectively in the presence of different types of uncertainties. Improving the versatility of the system and the algorithm employed, however, requires knowing of their limitations and implementing adaptive designs so that their performance can be improved.
Signal noise may be a significant contributing factor to these uncertainties. Noise may be represented mathematically, either through theoretical or empirical methods. The types of noise may also be modeled as an additive component affecting image intensities collected by the imaging system. For modeling purposes, image intensities of an observed image, {right arrow over (A)}ixj, may be defined as the sum of true intensities, I:
                                          A            ⇀                    ⁡                      (                                          x                i                            ,                              y                i                                      )                          =                                            ∑                                                x                  i                                ,                                  y                  i                                                      ⁢                                          I                ⇀                            ⁡                              (                                                      x                    i                                    ,                                      y                    i                                                  )                                              +                                                    n                1                            ⇀                        ⁡                          (                                                x                  i                                ,                                  y                  i                                            )                                +                                                                      n                  2                                ⇀                            ⁡                              (                                                      x                    i                                    ,                                      y                    i                                                  )                                      ⁢                                                  ⁢            …                    +                                                    n                k                            ⇀                        ⁡                          (                                                x                  i                                ,                                  y                  i                                            )                                                          Equation        ⁢                                  ⁢        1            wherein {right arrow over (nk)}(xi, yi) represents a noise source, k, associated with one or more of the uncertainties mentioned above. Noise may be represented as {right arrow over (N)}(xi, yi) and as the sum k. In so doing, Equation 1 may be simplified:{right arrow over (N)}(xi,yi)={right arrow over (n1)}(xi,yi)+{right arrow over (n2)}(xi,yi) . . . +{right arrow over (nk)}(xi,yi)  Equation 2
To show the effects of noise on image intensity, Equation 2 can be substituted in Equation 1:
                                          A            ⇀                    ⁡                      (                                          x                i                            ,                              y                i                                      )                          =                                            ∑                                                x                  i                                ,                                  y                  i                                                      ⁢                                          I                ⇀                            ⁡                              (                                                      x                    i                                    ,                                      y                    i                                                  )                                              +                                    N              ⇀                        ⁡                          (                                                x                  i                                ,                                  y                  i                                            )                                                          Equation        ⁢                                  ⁢        3            
Generally, filtering A(xi, yj) to remove the corrupting noise component, {right arrow over (N)}(xi, yj), would restore the image to the desired, noiseless form, I(xi, yj). However, neither I(xi, yj) nor N(xi, yj) are deterministic signals, which means filtering to remove {right arrow over (N)}(xi, yj), may be challenging, even when the noise sources are known.
Therefore, a need exists for characterizing limitations associated with an imaging system, and particularly the computer vision algorithm(s) it employs, to improve decision-making regarding system design, manufacturing, operation, tuning, and deployment.